Overview

Dataset statistics

Number of variables33
Number of observations4009
Missing cells58098
Missing cells (%)43.9%
Duplicate rows3
Duplicate rows (%)0.1%
Total size in memory1.0 MiB
Average record size in memory264.0 B

Variable types

Unsupported4
Categorical15
Numeric4
DateTime1
Text9

Alerts

opinion-of-the-field-of-study has constant value "useful"Constant
other-position has constant value "pkojihgufyudtyrstuiyguoipj"Constant
Dataset has 3 (0.1%) duplicate rowsDuplicates
Program-of-Study is highly overall correlated with statusHigh correlation
amount-of-salary-and-allownce is highly overall correlated with departement and 5 other fieldsHigh correlation
departement is highly overall correlated with amount-of-salary-and-allownce and 2 other fieldsHigh correlation
department is highly overall correlated with improvement-suggestions and 1 other fieldsHigh correlation
education-level is highly overall correlated with statusHigh correlation
employment-status is highly overall correlated with departement and 5 other fieldsHigh correlation
gender is highly overall correlated with amount-of-salary-and-allownceHigh correlation
graduation-year is highly overall correlated with product-or-service-offered and 1 other fieldsHigh correlation
improvement-suggestions is highly overall correlated with amount-of-salary-and-allownce and 6 other fieldsHigh correlation
industry-sector is highly overall correlated with amount-of-salary-and-allownce and 4 other fieldsHigh correlation
nik is highly overall correlated with position and 2 other fieldsHigh correlation
position is highly overall correlated with amount-of-salary-and-allownce and 5 other fieldsHigh correlation
product-or-service-offered is highly overall correlated with graduation-year and 2 other fieldsHigh correlation
quality-of-education is highly overall correlated with employment-status and 3 other fieldsHigh correlation
status is highly overall correlated with Program-of-Study and 10 other fieldsHigh correlation
unit-of-profit is highly overall correlated with graduation-year and 2 other fieldsHigh correlation
employment-status is highly imbalanced (99.4%)Imbalance
employment-status has 2000 (49.9%) missing valuesMissing
first-job has 2000 (49.9%) missing valuesMissing
year has 1000 (24.9%) missing valuesMissing
company-name has 2000 (49.9%) missing valuesMissing
industry-sector has 2000 (49.9%) missing valuesMissing
Address has 2000 (49.9%) missing valuesMissing
last-name has 2002 (49.9%) missing valuesMissing
department has 2002 (49.9%) missing valuesMissing
position has 2000 (49.9%) missing valuesMissing
opinion-of-the-field-of-study has 4002 (99.8%) missing valuesMissing
month has 1000 (24.9%) missing valuesMissing
salary-meet-the-minimum-wage has 2000 (49.9%) missing valuesMissing
first-name has 2002 (49.9%) missing valuesMissing
amount-of-salary-and-allownce has 2000 (49.9%) missing valuesMissing
other-position has 4008 (> 99.9%) missing valuesMissing
last_name has 2007 (50.1%) missing valuesMissing
email has 2007 (50.1%) missing valuesMissing
first_name has 2007 (50.1%) missing valuesMissing
departement has 2007 (50.1%) missing valuesMissing
education-level has 3009 (75.1%) missing valuesMissing
institution has 3009 (75.1%) missing valuesMissing
Program-of-Study has 3009 (75.1%) missing valuesMissing
product-or-service-offered has 3009 (75.1%) missing valuesMissing
amount-of-profit has 3009 (75.1%) missing valuesMissing
unit-of-profit has 3009 (75.1%) missing valuesMissing
nik is highly skewed (γ1 = 25.28997864)Skewed
_id is an unsupported type, check if it needs cleaning or further analysisUnsupported
first-job is an unsupported type, check if it needs cleaning or further analysisUnsupported
salary-meet-the-minimum-wage is an unsupported type, check if it needs cleaning or further analysisUnsupported
amount-of-profit is an unsupported type, check if it needs cleaning or further analysisUnsupported
graduation-year has 62 (1.5%) zerosZeros

Reproduction

Analysis started2024-07-12 08:57:19.355482
Analysis finished2024-07-12 08:57:35.275546
Duration15.92 seconds
Software versionydata-profiling v4.8.3
Download configurationconfig.json

Variables

_id
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size31.4 KiB

employment-status
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)0.1%
Missing2000
Missing (%)49.9%
Memory size31.4 KiB
daily-freelance
2008 
permanent
 
1

Length

Max length15
Median length15
Mean length14.997013
Min length9

Characters and Unicode

Total characters30129
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowdaily-freelance
2nd rowdaily-freelance
3rd rowpermanent
4th rowdaily-freelance
5th rowdaily-freelance

Common Values

ValueCountFrequency (%)
daily-freelance 2008
50.1%
permanent 1
 
< 0.1%
(Missing) 2000
49.9%

Length

2024-07-12T08:57:35.456124image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:35.909281image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
daily-freelance 2008
> 99.9%
permanent 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
e 6026
20.0%
a 4017
13.3%
l 4016
13.3%
n 2010
 
6.7%
r 2009
 
6.7%
d 2008
 
6.7%
i 2008
 
6.7%
y 2008
 
6.7%
- 2008
 
6.7%
f 2008
 
6.7%
Other values (4) 2011
 
6.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 30129
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6026
20.0%
a 4017
13.3%
l 4016
13.3%
n 2010
 
6.7%
r 2009
 
6.7%
d 2008
 
6.7%
i 2008
 
6.7%
y 2008
 
6.7%
- 2008
 
6.7%
f 2008
 
6.7%
Other values (4) 2011
 
6.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 30129
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6026
20.0%
a 4017
13.3%
l 4016
13.3%
n 2010
 
6.7%
r 2009
 
6.7%
d 2008
 
6.7%
i 2008
 
6.7%
y 2008
 
6.7%
- 2008
 
6.7%
f 2008
 
6.7%
Other values (4) 2011
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 30129
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6026
20.0%
a 4017
13.3%
l 4016
13.3%
n 2010
 
6.7%
r 2009
 
6.7%
d 2008
 
6.7%
i 2008
 
6.7%
y 2008
 
6.7%
- 2008
 
6.7%
f 2008
 
6.7%
Other values (4) 2011
 
6.7%

first-job
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2000
Missing (%)49.9%
Memory size31.4 KiB

year
Real number (ℝ)

MISSING 

Distinct59
Distinct (%)2.0%
Missing1000
Missing (%)24.9%
Infinite0
Infinite (%)0.0%
Mean2000.4646
Minimum1909
Maximum2024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-07-12T08:57:36.357801image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum1909
5-th percentile1986
Q11995
median2002
Q32008
95-th percentile2012
Maximum2024
Range115
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4675131
Coefficient of variation (CV)0.0047326571
Kurtosis7.848282
Mean2000.4646
Median Absolute Deviation (MAD)6
Skewness-1.7202442
Sum6019398
Variance89.633803
MonotonicityNot monotonic
2024-07-12T08:57:36.831583image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2006 159
 
4.0%
2011 156
 
3.9%
2004 155
 
3.9%
2009 155
 
3.9%
2008 147
 
3.7%
2007 147
 
3.7%
2012 134
 
3.3%
1992 128
 
3.2%
2010 128
 
3.2%
2003 127
 
3.2%
Other values (49) 1573
39.2%
(Missing) 1000
24.9%
ValueCountFrequency (%)
1909 2
 
< 0.1%
1953 1
 
< 0.1%
1955 1
 
< 0.1%
1958 1
 
< 0.1%
1959 2
 
< 0.1%
1960 2
 
< 0.1%
1962 1
 
< 0.1%
1963 3
 
0.1%
1964 6
0.1%
1965 9
0.2%
ValueCountFrequency (%)
2024 7
 
0.2%
2013 28
 
0.7%
2012 134
3.3%
2011 156
3.9%
2010 128
3.2%
2009 155
3.9%
2008 147
3.7%
2007 147
3.7%
2006 159
4.0%
2005 123
3.1%
Distinct495
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
Minimum2001-01-15 00:00:00
Maximum2024-07-11 00:00:00
2024-07-12T08:57:37.308213image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:37.738647image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

graduation-year
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct62
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1974.9414
Minimum0
Maximum2024
Zeros62
Zeros (%)1.5%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-07-12T08:57:38.274501image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1987
Q12000
median2008
Q32012
95-th percentile2018
Maximum2024
Range2024
Interquartile range (IQR)12

Descriptive statistics

Standard deviation247.7301
Coefficient of variation (CV)0.12543669
Kurtosis59.575265
Mean1974.9414
Median Absolute Deviation (MAD)6
Skewness-7.839233
Sum7917540
Variance61370.204
MonotonicityNot monotonic
2024-07-12T08:57:38.816550image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2011 251
 
6.3%
2012 243
 
6.1%
2010 203
 
5.1%
2014 187
 
4.7%
2006 187
 
4.7%
2009 180
 
4.5%
2008 167
 
4.2%
2004 156
 
3.9%
2013 155
 
3.9%
2015 154
 
3.8%
Other values (52) 2126
53.0%
ValueCountFrequency (%)
0 62
1.5%
1948 1
 
< 0.1%
1957 1
 
< 0.1%
1958 1
 
< 0.1%
1959 1
 
< 0.1%
1960 1
 
< 0.1%
1961 1
 
< 0.1%
1963 2
 
< 0.1%
1964 6
 
0.1%
1965 2
 
< 0.1%
ValueCountFrequency (%)
2024 1
 
< 0.1%
2021 12
 
0.3%
2020 93
2.3%
2019 91
2.3%
2018 84
2.1%
2017 96
2.4%
2016 119
3.0%
2015 154
3.8%
2014 187
4.7%
2013 155
3.9%

company-name
Text

MISSING 

Distinct831
Distinct (%)41.4%
Missing2000
Missing (%)49.9%
Memory size31.4 KiB
2024-07-12T08:57:39.572354image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length99
Median length55
Mean length23.365356
Min length2

Characters and Unicode

Total characters46941
Distinct characters72
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique527 ?
Unique (%)26.2%

Sample

1st rowJIVALOKA
2nd rowJIVALOKA
3rd rowPT Ijoroyo
4th rowJIVALOKA
5th rowJIVALOKA
ValueCountFrequency (%)
inc 909
 
14.1%
llc 239
 
3.7%
pharmaceuticals 237
 
3.7%
laboratories 182
 
2.8%
company 133
 
2.1%
health 103
 
1.6%
co 89
 
1.4%
corporation 83
 
1.3%
ltd 81
 
1.3%
81
 
1.3%
Other values (1047) 4331
67.0%
2024-07-12T08:57:40.781793image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4459
 
9.5%
a 3769
 
8.0%
e 2809
 
6.0%
r 2613
 
5.6%
n 2504
 
5.3%
o 2293
 
4.9%
c 2285
 
4.9%
i 2204
 
4.7%
t 1996
 
4.3%
l 1617
 
3.4%
Other values (62) 20392
43.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 46941
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4459
 
9.5%
a 3769
 
8.0%
e 2809
 
6.0%
r 2613
 
5.6%
n 2504
 
5.3%
o 2293
 
4.9%
c 2285
 
4.9%
i 2204
 
4.7%
t 1996
 
4.3%
l 1617
 
3.4%
Other values (62) 20392
43.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 46941
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4459
 
9.5%
a 3769
 
8.0%
e 2809
 
6.0%
r 2613
 
5.6%
n 2504
 
5.3%
o 2293
 
4.9%
c 2285
 
4.9%
i 2204
 
4.7%
t 1996
 
4.3%
l 1617
 
3.4%
Other values (62) 20392
43.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 46941
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4459
 
9.5%
a 3769
 
8.0%
e 2809
 
6.0%
r 2613
 
5.6%
n 2504
 
5.3%
o 2293
 
4.9%
c 2285
 
4.9%
i 2204
 
4.7%
t 1996
 
4.3%
l 1617
 
3.4%
Other values (62) 20392
43.4%

industry-sector
Categorical

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)0.7%
Missing2000
Missing (%)49.9%
Memory size31.4 KiB
n/a
437 
Finance
343 
Health Care
236 
Consumer Services
233 
Technology
194 
Other values (10)
566 

Length

Max length21
Median length16
Mean length10.10005
Min length3

Characters and Unicode

Total characters20291
Distinct characters37
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowmarketing
2nd rowmarketing
3rd rowother
4th rowmarketing
5th rowmarketing

Common Values

ValueCountFrequency (%)
n/a 437
 
10.9%
Finance 343
 
8.6%
Health Care 236
 
5.9%
Consumer Services 233
 
5.8%
Technology 194
 
4.8%
Capital Goods 111
 
2.8%
Public Utilities 89
 
2.2%
Energy 81
 
2.0%
Consumer Non-Durables 73
 
1.8%
Basic Industries 69
 
1.7%
Other values (5) 143
 
3.6%
(Missing) 2000
49.9%

Length

2024-07-12T08:57:41.118172image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
n/a 437
15.2%
consumer 362
12.6%
finance 343
11.9%
health 236
8.2%
care 236
8.2%
services 233
8.1%
technology 194
 
6.7%
capital 111
 
3.9%
goods 111
 
3.9%
utilities 89
 
3.1%
Other values (10) 524
18.2%

Most occurring characters

ValueCountFrequency (%)
e 2290
 
11.3%
n 2029
 
10.0%
a 1799
 
8.9%
i 1267
 
6.2%
s 1250
 
6.2%
r 1199
 
5.9%
o 1167
 
5.8%
c 967
 
4.8%
l 926
 
4.6%
867
 
4.3%
Other values (27) 6530
32.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 20291
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2290
 
11.3%
n 2029
 
10.0%
a 1799
 
8.9%
i 1267
 
6.2%
s 1250
 
6.2%
r 1199
 
5.9%
o 1167
 
5.8%
c 967
 
4.8%
l 926
 
4.6%
867
 
4.3%
Other values (27) 6530
32.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 20291
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2290
 
11.3%
n 2029
 
10.0%
a 1799
 
8.9%
i 1267
 
6.2%
s 1250
 
6.2%
r 1199
 
5.9%
o 1167
 
5.8%
c 967
 
4.8%
l 926
 
4.6%
867
 
4.3%
Other values (27) 6530
32.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 20291
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2290
 
11.3%
n 2029
 
10.0%
a 1799
 
8.9%
i 1267
 
6.2%
s 1250
 
6.2%
r 1199
 
5.9%
o 1167
 
5.8%
c 967
 
4.8%
l 926
 
4.6%
867
 
4.3%
Other values (27) 6530
32.2%

Address
Text

MISSING 

Distinct1271
Distinct (%)63.3%
Missing2000
Missing (%)49.9%
Memory size31.4 KiB
2024-07-12T08:57:41.606745image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length12
Mean length9.0886013
Min length5

Characters and Unicode

Total characters18259
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1096 ?
Unique (%)54.6%

Sample

1st rowJl . Bumi Karadenan Permai
2nd rowJl . Bumi Karadenan Permai
3rd rowCibinong
4th rowJl . Bumi Karadenan Permai
5th rowJl . Bumi Karadenan Permai
ValueCountFrequency (%)
apt 417
 
9.4%
suite 416
 
9.4%
box 411
 
9.2%
po 411
 
9.2%
room 396
 
8.9%
floor 362
 
8.1%
1st 23
 
0.5%
15th 23
 
0.5%
10th 22
 
0.5%
4th 22
 
0.5%
Other values (1169) 1943
43.7%
2024-07-12T08:57:42.391593image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2437
 
13.3%
o 1928
 
10.6%
1 1194
 
6.5%
t 1167
 
6.4%
4 616
 
3.4%
5 596
 
3.3%
2 585
 
3.2%
8 577
 
3.2%
9 571
 
3.1%
7 553
 
3.0%
Other values (29) 8035
44.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2437
 
13.3%
o 1928
 
10.6%
1 1194
 
6.5%
t 1167
 
6.4%
4 616
 
3.4%
5 596
 
3.3%
2 585
 
3.2%
8 577
 
3.2%
9 571
 
3.1%
7 553
 
3.0%
Other values (29) 8035
44.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2437
 
13.3%
o 1928
 
10.6%
1 1194
 
6.5%
t 1167
 
6.4%
4 616
 
3.4%
5 596
 
3.3%
2 585
 
3.2%
8 577
 
3.2%
9 571
 
3.1%
7 553
 
3.0%
Other values (29) 8035
44.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2437
 
13.3%
o 1928
 
10.6%
1 1194
 
6.5%
t 1167
 
6.4%
4 616
 
3.4%
5 596
 
3.3%
2 585
 
3.2%
8 577
 
3.2%
9 571
 
3.1%
7 553
 
3.0%
Other values (29) 8035
44.0%

quality-of-education
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
poor
1165 
good
1163 
exellent
680 
excellent
501 
not-satisfied
500 

Length

Max length13
Median length4
Mean length6.425792
Min length4

Characters and Unicode

Total characters25761
Distinct characters16
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowgood
3rd rowexcellent
4th rowgood
5th rowgood

Common Values

ValueCountFrequency (%)
poor 1165
29.1%
good 1163
29.0%
exellent 680
17.0%
excellent 501
12.5%
not-satisfied 500
12.5%

Length

2024-07-12T08:57:42.685254image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:42.994654image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
poor 1165
29.1%
good 1163
29.0%
exellent 680
17.0%
excellent 501
12.5%
not-satisfied 500
12.5%

Most occurring characters

ValueCountFrequency (%)
o 5156
20.0%
e 4043
15.7%
l 2362
9.2%
t 2181
8.5%
n 1681
 
6.5%
d 1663
 
6.5%
x 1181
 
4.6%
p 1165
 
4.5%
r 1165
 
4.5%
g 1163
 
4.5%
Other values (6) 4001
15.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 25761
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 5156
20.0%
e 4043
15.7%
l 2362
9.2%
t 2181
8.5%
n 1681
 
6.5%
d 1663
 
6.5%
x 1181
 
4.6%
p 1165
 
4.5%
r 1165
 
4.5%
g 1163
 
4.5%
Other values (6) 4001
15.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 25761
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 5156
20.0%
e 4043
15.7%
l 2362
9.2%
t 2181
8.5%
n 1681
 
6.5%
d 1663
 
6.5%
x 1181
 
4.6%
p 1165
 
4.5%
r 1165
 
4.5%
g 1163
 
4.5%
Other values (6) 4001
15.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 25761
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 5156
20.0%
e 4043
15.7%
l 2362
9.2%
t 2181
8.5%
n 1681
 
6.5%
d 1663
 
6.5%
x 1181
 
4.6%
p 1165
 
4.5%
r 1165
 
4.5%
g 1163
 
4.5%
Other values (6) 4001
15.5%

last-name
Text

MISSING 

Distinct1967
Distinct (%)98.0%
Missing2002
Missing (%)49.9%
Memory size31.4 KiB
2024-07-12T08:57:43.463507image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length25
Median length14
Mean length7.019432
Min length3

Characters and Unicode

Total characters14088
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1931 ?
Unique (%)96.2%

Sample

1st rowSeptanugroho
2nd rowSeptanugroho
3rd rowIsmail
4th rowSeptanugroho
5th rowSeptanugroho
ValueCountFrequency (%)
de 8
 
0.4%
septanugroho 6
 
0.3%
le 5
 
0.2%
o 4
 
0.2%
van 4
 
0.2%
la 3
 
0.1%
marlow 2
 
0.1%
pring 2
 
0.1%
bythway 2
 
0.1%
pedrol 2
 
0.1%
Other values (1970) 2002
98.1%
2024-07-12T08:57:44.279135image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1529
 
10.9%
a 1109
 
7.9%
r 1052
 
7.5%
n 951
 
6.8%
l 928
 
6.6%
o 875
 
6.2%
i 862
 
6.1%
t 652
 
4.6%
s 568
 
4.0%
d 397
 
2.8%
Other values (46) 5165
36.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 14088
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1529
 
10.9%
a 1109
 
7.9%
r 1052
 
7.5%
n 951
 
6.8%
l 928
 
6.6%
o 875
 
6.2%
i 862
 
6.1%
t 652
 
4.6%
s 568
 
4.0%
d 397
 
2.8%
Other values (46) 5165
36.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 14088
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1529
 
10.9%
a 1109
 
7.9%
r 1052
 
7.5%
n 951
 
6.8%
l 928
 
6.6%
o 875
 
6.2%
i 862
 
6.1%
t 652
 
4.6%
s 568
 
4.0%
d 397
 
2.8%
Other values (46) 5165
36.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 14088
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1529
 
10.9%
a 1109
 
7.9%
r 1052
 
7.5%
n 951
 
6.8%
l 928
 
6.6%
o 875
 
6.2%
i 862
 
6.1%
t 652
 
4.6%
s 568
 
4.0%
d 397
 
2.8%
Other values (46) 5165
36.7%

department
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.7%
Missing2002
Missing (%)49.9%
Memory size31.4 KiB
Accounting
186 
Sales
184 
Business Development
183 
Training
182 
Legal
173 
Other values (9)
1099 

Length

Max length24
Median length18
Mean length11.501744
Min length5

Characters and Unicode

Total characters23084
Distinct characters32
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowteknik-otomasi-industri
2nd rowteknik-otomasi-industri
3rd rowrekayasa-perangkat-lunak
4th rowteknik-otomasi-industri
5th rowteknik-otomasi-industri

Common Values

ValueCountFrequency (%)
Accounting 186
 
4.6%
Sales 184
 
4.6%
Business Development 183
 
4.6%
Training 182
 
4.5%
Legal 173
 
4.3%
Engineering 164
 
4.1%
Marketing 163
 
4.1%
Services 163
 
4.1%
Product Management 157
 
3.9%
Human Resources 156
 
3.9%
Other values (4) 296
 
7.4%
(Missing) 2002
49.9%

Length

2024-07-12T08:57:44.619381image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
development 324
 
11.6%
accounting 186
 
6.7%
sales 184
 
6.6%
business 183
 
6.6%
training 182
 
6.5%
legal 173
 
6.2%
engineering 164
 
5.9%
marketing 163
 
5.9%
services 163
 
5.9%
management 157
 
5.6%
Other values (8) 906
32.5%

Most occurring characters

ValueCountFrequency (%)
e 3245
14.1%
n 2523
 
10.9%
a 1466
 
6.4%
i 1411
 
6.1%
s 1362
 
5.9%
r 1282
 
5.6%
g 1190
 
5.2%
t 1154
 
5.0%
u 993
 
4.3%
c 989
 
4.3%
Other values (22) 7469
32.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23084
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3245
14.1%
n 2523
 
10.9%
a 1466
 
6.4%
i 1411
 
6.1%
s 1362
 
5.9%
r 1282
 
5.6%
g 1190
 
5.2%
t 1154
 
5.0%
u 993
 
4.3%
c 989
 
4.3%
Other values (22) 7469
32.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23084
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3245
14.1%
n 2523
 
10.9%
a 1466
 
6.4%
i 1411
 
6.1%
s 1362
 
5.9%
r 1282
 
5.6%
g 1190
 
5.2%
t 1154
 
5.0%
u 993
 
4.3%
c 989
 
4.3%
Other values (22) 7469
32.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23084
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3245
14.1%
n 2523
 
10.9%
a 1466
 
6.4%
i 1411
 
6.1%
s 1362
 
5.9%
r 1282
 
5.6%
g 1190
 
5.2%
t 1154
 
5.0%
u 993
 
4.3%
c 989
 
4.3%
Other values (22) 7469
32.4%

status
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
working
2009 
studying
1000 
entrepreneur
1000 

Length

Max length12
Median length7
Mean length8.4966326
Min length7

Characters and Unicode

Total characters34063
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowworking
2nd rowworking
3rd rowworking
4th rowworking
5th rowworking

Common Values

ValueCountFrequency (%)
working 2009
50.1%
studying 1000
24.9%
entrepreneur 1000
24.9%

Length

2024-07-12T08:57:44.921411image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:45.225379image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
working 2009
50.1%
studying 1000
24.9%
entrepreneur 1000
24.9%

Most occurring characters

ValueCountFrequency (%)
r 5009
14.7%
n 5009
14.7%
e 4000
11.7%
i 3009
8.8%
g 3009
8.8%
w 2009
5.9%
o 2009
5.9%
k 2009
5.9%
t 2000
 
5.9%
u 2000
 
5.9%
Other values (4) 4000
11.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 34063
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 5009
14.7%
n 5009
14.7%
e 4000
11.7%
i 3009
8.8%
g 3009
8.8%
w 2009
5.9%
o 2009
5.9%
k 2009
5.9%
t 2000
 
5.9%
u 2000
 
5.9%
Other values (4) 4000
11.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 34063
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 5009
14.7%
n 5009
14.7%
e 4000
11.7%
i 3009
8.8%
g 3009
8.8%
w 2009
5.9%
o 2009
5.9%
k 2009
5.9%
t 2000
 
5.9%
u 2000
 
5.9%
Other values (4) 4000
11.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 34063
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 5009
14.7%
n 5009
14.7%
e 4000
11.7%
i 3009
8.8%
g 3009
8.8%
w 2009
5.9%
o 2009
5.9%
k 2009
5.9%
t 2000
 
5.9%
u 2000
 
5.9%
Other values (4) 4000
11.7%

position
Categorical

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)0.7%
Missing2000
Missing (%)49.9%
Memory size31.4 KiB
Marketing
181 
Training
181 
Legal
177 
Research and Development
175 
Sales
171 
Other values (9)
1124 

Length

Max length24
Median length15
Mean length11.587855
Min length5

Characters and Unicode

Total characters23280
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowaccounting
2nd rowaccounting
3rd rowother
4th rowaccounting
5th rowaccounting

Common Values

ValueCountFrequency (%)
Marketing 181
 
4.5%
Training 181
 
4.5%
Legal 177
 
4.4%
Research and Development 175
 
4.4%
Sales 171
 
4.3%
Human Resources 170
 
4.2%
Services 169
 
4.2%
Business Development 166
 
4.1%
Accounting 162
 
4.0%
Engineering 161
 
4.0%
Other values (4) 296
 
7.4%
(Missing) 2000
49.9%

Length

2024-07-12T08:57:45.494547image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
development 341
 
12.0%
marketing 181
 
6.4%
training 181
 
6.4%
legal 177
 
6.2%
research 175
 
6.2%
and 175
 
6.2%
sales 171
 
6.0%
resources 170
 
6.0%
human 170
 
6.0%
services 169
 
6.0%
Other values (7) 927
32.7%

Most occurring characters

ValueCountFrequency (%)
e 3353
14.4%
n 2498
 
10.7%
a 1520
 
6.5%
i 1368
 
5.9%
s 1353
 
5.8%
r 1327
 
5.7%
g 1171
 
5.0%
t 1122
 
4.8%
c 992
 
4.3%
o 969
 
4.2%
Other values (20) 7607
32.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 23280
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 3353
14.4%
n 2498
 
10.7%
a 1520
 
6.5%
i 1368
 
5.9%
s 1353
 
5.8%
r 1327
 
5.7%
g 1171
 
5.0%
t 1122
 
4.8%
c 992
 
4.3%
o 969
 
4.2%
Other values (20) 7607
32.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 23280
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 3353
14.4%
n 2498
 
10.7%
a 1520
 
6.5%
i 1368
 
5.9%
s 1353
 
5.8%
r 1327
 
5.7%
g 1171
 
5.0%
t 1122
 
4.8%
c 992
 
4.3%
o 969
 
4.2%
Other values (20) 7607
32.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 23280
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 3353
14.4%
n 2498
 
10.7%
a 1520
 
6.5%
i 1368
 
5.9%
s 1353
 
5.8%
r 1327
 
5.7%
g 1171
 
5.0%
t 1122
 
4.8%
c 992
 
4.3%
o 969
 
4.2%
Other values (20) 7607
32.7%

opinion-of-the-field-of-study
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)14.3%
Missing4002
Missing (%)99.8%
Memory size31.4 KiB
useful

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters42
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuseful
2nd rowuseful
3rd rowuseful
4th rowuseful
5th rowuseful

Common Values

ValueCountFrequency (%)
useful 7
 
0.2%
(Missing) 4002
99.8%

Length

2024-07-12T08:57:46.184590image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:46.615776image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
useful 7
100.0%

Most occurring characters

ValueCountFrequency (%)
u 14
33.3%
s 7
16.7%
e 7
16.7%
f 7
16.7%
l 7
16.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 14
33.3%
s 7
16.7%
e 7
16.7%
f 7
16.7%
l 7
16.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 14
33.3%
s 7
16.7%
e 7
16.7%
f 7
16.7%
l 7
16.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 42
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 14
33.3%
s 7
16.7%
e 7
16.7%
f 7
16.7%
l 7
16.7%

gender
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
Male
1801 
Female
1792 
Polygender
 
77
Agender
 
73
Genderqueer
 
73
Other values (4)
193 

Length

Max length11
Median length10
Mean length5.4554752
Min length4

Characters and Unicode

Total characters21871
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowmale
2nd rowmale
3rd rowmale
4th rowmale
5th rowmale

Common Values

ValueCountFrequency (%)
Male 1801
44.9%
Female 1792
44.7%
Polygender 77
 
1.9%
Agender 73
 
1.8%
Genderqueer 73
 
1.8%
Non-binary 69
 
1.7%
Genderfluid 59
 
1.5%
Bigender 58
 
1.4%
male 7
 
0.2%

Length

2024-07-12T08:57:47.047402image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:47.566245image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
male 1808
45.1%
female 1792
44.7%
polygender 77
 
1.9%
agender 73
 
1.8%
genderqueer 73
 
1.8%
non-binary 69
 
1.7%
genderfluid 59
 
1.5%
bigender 58
 
1.4%

Most occurring characters

ValueCountFrequency (%)
e 6218
28.4%
l 3736
17.1%
a 3669
16.8%
M 1801
 
8.2%
m 1799
 
8.2%
F 1792
 
8.2%
r 482
 
2.2%
n 478
 
2.2%
d 399
 
1.8%
g 208
 
1.0%
Other values (13) 1289
 
5.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21871
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 6218
28.4%
l 3736
17.1%
a 3669
16.8%
M 1801
 
8.2%
m 1799
 
8.2%
F 1792
 
8.2%
r 482
 
2.2%
n 478
 
2.2%
d 399
 
1.8%
g 208
 
1.0%
Other values (13) 1289
 
5.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21871
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 6218
28.4%
l 3736
17.1%
a 3669
16.8%
M 1801
 
8.2%
m 1799
 
8.2%
F 1792
 
8.2%
r 482
 
2.2%
n 478
 
2.2%
d 399
 
1.8%
g 208
 
1.0%
Other values (13) 1289
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21871
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 6218
28.4%
l 3736
17.1%
a 3669
16.8%
M 1801
 
8.2%
m 1799
 
8.2%
F 1792
 
8.2%
r 482
 
2.2%
n 478
 
2.2%
d 399
 
1.8%
g 208
 
1.0%
Other values (13) 1289
 
5.9%

month
Categorical

MISSING 

Distinct12
Distinct (%)0.4%
Missing1000
Missing (%)24.9%
Memory size31.4 KiB
september
261 
may
261 
january
260 
april
258 
july
256 
Other values (7)
1713 

Length

Max length9
Median length7
Mean length6.148222
Min length3

Characters and Unicode

Total characters18500
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowoctober
2nd rowoctober
3rd rowapril
4th rowoctober
5th rowoctober

Common Values

ValueCountFrequency (%)
september 261
 
6.5%
may 261
 
6.5%
january 260
 
6.5%
april 258
 
6.4%
july 256
 
6.4%
august 254
 
6.3%
december 252
 
6.3%
march 249
 
6.2%
october 243
 
6.1%
june 243
 
6.1%
Other values (2) 472
11.8%
(Missing) 1000
24.9%

Length

2024-07-12T08:57:48.052206image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
september 261
8.7%
may 261
8.7%
january 260
8.6%
april 258
8.6%
july 256
8.5%
august 254
8.4%
december 252
8.4%
march 249
8.3%
october 243
8.1%
june 243
8.1%
Other values (2) 472
15.7%

Most occurring characters

ValueCountFrequency (%)
e 2737
14.8%
r 2227
12.0%
a 1774
 
9.6%
u 1499
 
8.1%
m 1263
 
6.8%
b 1228
 
6.6%
y 1009
 
5.5%
j 759
 
4.1%
t 758
 
4.1%
c 744
 
4.0%
Other values (11) 4502
24.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 18500
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2737
14.8%
r 2227
12.0%
a 1774
 
9.6%
u 1499
 
8.1%
m 1263
 
6.8%
b 1228
 
6.6%
y 1009
 
5.5%
j 759
 
4.1%
t 758
 
4.1%
c 744
 
4.0%
Other values (11) 4502
24.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 18500
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2737
14.8%
r 2227
12.0%
a 1774
 
9.6%
u 1499
 
8.1%
m 1263
 
6.8%
b 1228
 
6.6%
y 1009
 
5.5%
j 759
 
4.1%
t 758
 
4.1%
c 744
 
4.0%
Other values (11) 4502
24.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 18500
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2737
14.8%
r 2227
12.0%
a 1774
 
9.6%
u 1499
 
8.1%
m 1263
 
6.8%
b 1228
 
6.6%
y 1009
 
5.5%
j 759
 
4.1%
t 758
 
4.1%
c 744
 
4.0%
Other values (11) 4502
24.3%

salary-meet-the-minimum-wage
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2000
Missing (%)49.9%
Memory size31.4 KiB

improvement-suggestions
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.4 KiB
hard to get a job for a while
681 
it's hard for getting job after school life
666 
great i'm joinning top university
655 
saya berhasil masuk ptn impian
500 
saya berhasil masuk ptn walaupun bukan impian saya
500 
Other values (5)
1007 

Length

Max length52
Median length36
Mean length38.448241
Min length14

Characters and Unicode

Total characters154139
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowaegbahwg9yhagw
2nd rowaegbahwg9yhagw
3rd rowoiufyutycuvbjoigouiyctu
4th rowimprove the cleaning services
5th rowaegbahwg9yhagw

Common Values

ValueCountFrequency (%)
hard to get a job for a while 681
17.0%
it's hard for getting job after school life 666
16.6%
great i'm joinning top university 655
16.3%
saya berhasil masuk ptn impian 500
12.5%
saya berhasil masuk ptn walaupun bukan impian saya 500
12.5%
saya sangat sulit untuk masuk kuliah 500
12.5%
saya bisa masuk kuliah walaupun harus bersusah payah 500
12.5%
aegbahwg9yhagw 4
 
0.1%
improve the cleaning services 2
 
< 0.1%
oiufyutycuvbjoigouiyctu 1
 
< 0.1%

Length

2024-07-12T08:57:48.554370image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:49.176001image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
saya 2500
 
9.1%
masuk 2000
 
7.3%
a 1362
 
4.9%
for 1347
 
4.9%
hard 1347
 
4.9%
job 1347
 
4.9%
berhasil 1000
 
3.6%
kuliah 1000
 
3.6%
walaupun 1000
 
3.6%
ptn 1000
 
3.6%
Other values (28) 13661
49.6%

Most occurring characters

ValueCountFrequency (%)
23555
15.3%
a 20044
13.0%
i 10963
 
7.1%
s 10491
 
6.8%
u 8660
 
5.6%
t 8495
 
5.5%
n 7790
 
5.1%
r 6674
 
4.3%
h 6204
 
4.0%
e 6184
 
4.0%
Other values (16) 45079
29.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 154139
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
23555
15.3%
a 20044
13.0%
i 10963
 
7.1%
s 10491
 
6.8%
u 8660
 
5.6%
t 8495
 
5.5%
n 7790
 
5.1%
r 6674
 
4.3%
h 6204
 
4.0%
e 6184
 
4.0%
Other values (16) 45079
29.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 154139
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
23555
15.3%
a 20044
13.0%
i 10963
 
7.1%
s 10491
 
6.8%
u 8660
 
5.6%
t 8495
 
5.5%
n 7790
 
5.1%
r 6674
 
4.3%
h 6204
 
4.0%
e 6184
 
4.0%
Other values (16) 45079
29.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 154139
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
23555
15.3%
a 20044
13.0%
i 10963
 
7.1%
s 10491
 
6.8%
u 8660
 
5.6%
t 8495
 
5.5%
n 7790
 
5.1%
r 6674
 
4.3%
h 6204
 
4.0%
e 6184
 
4.0%
Other values (16) 45079
29.2%

first-name
Text

MISSING 

Distinct1775
Distinct (%)88.4%
Missing2002
Missing (%)49.9%
Memory size31.4 KiB
2024-07-12T08:57:50.117193image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.9646238
Min length2

Characters and Unicode

Total characters11971
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1571 ?
Unique (%)78.3%

Sample

1st rowMuhammad Aris
2nd rowMuhammad Aris
3rd rowFadli
4th rowMuhammad Aris
5th rowMuhammad Aris
ValueCountFrequency (%)
muhammad 6
 
0.3%
aris 6
 
0.3%
ann-marie 4
 
0.2%
ricky 3
 
0.1%
moe 3
 
0.1%
andrus 3
 
0.1%
woodman 3
 
0.1%
evyn 3
 
0.1%
kalie 3
 
0.1%
hubie 3
 
0.1%
Other values (1766) 1976
98.2%
2024-07-12T08:57:51.665932image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1382
 
11.5%
a 1262
 
10.5%
i 1027
 
8.6%
n 918
 
7.7%
r 878
 
7.3%
l 829
 
6.9%
o 605
 
5.1%
t 468
 
3.9%
y 356
 
3.0%
d 353
 
2.9%
Other values (43) 3893
32.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11971
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1382
 
11.5%
a 1262
 
10.5%
i 1027
 
8.6%
n 918
 
7.7%
r 878
 
7.3%
l 829
 
6.9%
o 605
 
5.1%
t 468
 
3.9%
y 356
 
3.0%
d 353
 
2.9%
Other values (43) 3893
32.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11971
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1382
 
11.5%
a 1262
 
10.5%
i 1027
 
8.6%
n 918
 
7.7%
r 878
 
7.3%
l 829
 
6.9%
o 605
 
5.1%
t 468
 
3.9%
y 356
 
3.0%
d 353
 
2.9%
Other values (43) 3893
32.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11971
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1382
 
11.5%
a 1262
 
10.5%
i 1027
 
8.6%
n 918
 
7.7%
r 878
 
7.3%
l 829
 
6.9%
o 605
 
5.1%
t 468
 
3.9%
y 356
 
3.0%
d 353
 
2.9%
Other values (43) 3893
32.5%

nik
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct4003
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0509333 × 1012
Minimum2789728
Maximum3.2010104 × 1015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-07-12T08:57:52.235358image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum2789728
5-th percentile5.615157 × 108
Q12.5683375 × 109
median4.9640021 × 109
Q37.536463 × 109
95-th percentile9.484503 × 109
Maximum3.2010104 × 1015
Range3.2010104 × 1015
Interquartile range (IQR)4.9681255 × 109

Descriptive statistics

Standard deviation1.2479845 × 1014
Coefficient of variation (CV)24.707998
Kurtosis642.92936
Mean5.0509333 × 1012
Median Absolute Deviation (MAD)2.4884714 × 109
Skewness25.289979
Sum2.0249192 × 1016
Variance1.5574653 × 1028
MonotonicityNot monotonic
2024-07-12T08:57:52.745077image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.20101044 × 10156
 
0.1%
364263113 2
 
< 0.1%
7359596084 1
 
< 0.1%
9613605398 1
 
< 0.1%
3913654992 1
 
< 0.1%
1349899720 1
 
< 0.1%
3413832425 1
 
< 0.1%
9026399286 1
 
< 0.1%
2302489101 1
 
< 0.1%
5504508622 1
 
< 0.1%
Other values (3993) 3993
99.6%
ValueCountFrequency (%)
2789728 1
< 0.1%
11768266 1
< 0.1%
11973110 1
< 0.1%
12698601 1
< 0.1%
15481883 1
< 0.1%
27305635 1
< 0.1%
30557143 1
< 0.1%
38172054 1
< 0.1%
49084534 1
< 0.1%
49558374 1
< 0.1%
ValueCountFrequency (%)
3.20101044 × 10156
0.1%
1.023073197 × 10151
 
< 0.1%
9999197620 1
 
< 0.1%
9998176859 1
 
< 0.1%
9997159225 1
 
< 0.1%
9996511863 1
 
< 0.1%
9994461982 1
 
< 0.1%
9990387109 1
 
< 0.1%
9981592773 1
 
< 0.1%
9980664274 1
 
< 0.1%

amount-of-salary-and-allownce
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1987
Distinct (%)98.9%
Missing2000
Missing (%)49.9%
Infinite0
Infinite (%)0.0%
Mean414191.87
Minimum148
Maximum1.2083402 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size31.4 KiB
2024-07-12T08:57:53.264962image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Quantile statistics

Minimum148
5-th percentile5808.6
Q126813
median51891
Q376090
95-th percentile95303.8
Maximum1.2083402 × 108
Range1.2083387 × 108
Interquartile range (IQR)49277

Descriptive statistics

Standard deviation6593351.9
Coefficient of variation (CV)15.918593
Kurtosis330.46217
Mean414191.87
Median Absolute Deviation (MAD)24747
Skewness18.221969
Sum8.3211146 × 108
Variance4.3472289 × 1013
MonotonicityNot monotonic
2024-07-12T08:57:53.789955image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
120834021 6
 
0.1%
99351 2
 
< 0.1%
56643 2
 
< 0.1%
81963 2
 
< 0.1%
80460 2
 
< 0.1%
21418 2
 
< 0.1%
38828 2
 
< 0.1%
78148 2
 
< 0.1%
99761 2
 
< 0.1%
36133 2
 
< 0.1%
Other values (1977) 1985
49.5%
(Missing) 2000
49.9%
ValueCountFrequency (%)
148 1
< 0.1%
171 1
< 0.1%
176 1
< 0.1%
248 1
< 0.1%
309 1
< 0.1%
417 1
< 0.1%
498 1
< 0.1%
566 1
< 0.1%
582 1
< 0.1%
614 1
< 0.1%
ValueCountFrequency (%)
120834021 6
0.1%
5000000 1
 
< 0.1%
99961 1
 
< 0.1%
99960 1
 
< 0.1%
99893 1
 
< 0.1%
99815 1
 
< 0.1%
99785 1
 
< 0.1%
99761 2
 
< 0.1%
99731 1
 
< 0.1%
99689 1
 
< 0.1%

other-position
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing4008
Missing (%)> 99.9%
Memory size31.4 KiB
2024-07-12T08:57:54.286239image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length26
Median length26
Mean length26
Min length26

Characters and Unicode

Total characters26
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowpkojihgufyudtyrstuiyguoipj
ValueCountFrequency (%)
pkojihgufyudtyrstuiyguoipj 1
100.0%
2024-07-12T08:57:55.236808image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
u 4
15.4%
i 3
11.5%
y 3
11.5%
p 2
7.7%
o 2
7.7%
j 2
7.7%
g 2
7.7%
t 2
7.7%
k 1
 
3.8%
h 1
 
3.8%
Other values (4) 4
15.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u 4
15.4%
i 3
11.5%
y 3
11.5%
p 2
7.7%
o 2
7.7%
j 2
7.7%
g 2
7.7%
t 2
7.7%
k 1
 
3.8%
h 1
 
3.8%
Other values (4) 4
15.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u 4
15.4%
i 3
11.5%
y 3
11.5%
p 2
7.7%
o 2
7.7%
j 2
7.7%
g 2
7.7%
t 2
7.7%
k 1
 
3.8%
h 1
 
3.8%
Other values (4) 4
15.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 26
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u 4
15.4%
i 3
11.5%
y 3
11.5%
p 2
7.7%
o 2
7.7%
j 2
7.7%
g 2
7.7%
t 2
7.7%
k 1
 
3.8%
h 1
 
3.8%
Other values (4) 4
15.4%

last_name
Text

MISSING 

Distinct1966
Distinct (%)98.2%
Missing2007
Missing (%)50.1%
Memory size31.4 KiB
2024-07-12T08:57:55.854377image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.9905095
Min length3

Characters and Unicode

Total characters13995
Distinct characters55
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1930 ?
Unique (%)96.4%

Sample

1st rowJosefer
2nd rowJosefer
3rd rowOlorenshaw
4th rowAverall
5th rowRagg
ValueCountFrequency (%)
de 11
 
0.5%
mc 6
 
0.3%
le 4
 
0.2%
laxon 2
 
0.1%
drynan 2
 
0.1%
perfili 2
 
0.1%
lyptrit 2
 
0.1%
janosevic 2
 
0.1%
millwater 2
 
0.1%
sapsed 2
 
0.1%
Other values (1965) 1997
98.3%
2024-07-12T08:57:56.639843image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1514
 
10.8%
a 1032
 
7.4%
r 987
 
7.1%
n 946
 
6.8%
l 915
 
6.5%
o 913
 
6.5%
i 890
 
6.4%
t 678
 
4.8%
s 591
 
4.2%
d 408
 
2.9%
Other values (45) 5121
36.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13995
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1514
 
10.8%
a 1032
 
7.4%
r 987
 
7.1%
n 946
 
6.8%
l 915
 
6.5%
o 913
 
6.5%
i 890
 
6.4%
t 678
 
4.8%
s 591
 
4.2%
d 408
 
2.9%
Other values (45) 5121
36.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13995
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1514
 
10.8%
a 1032
 
7.4%
r 987
 
7.1%
n 946
 
6.8%
l 915
 
6.5%
o 913
 
6.5%
i 890
 
6.4%
t 678
 
4.8%
s 591
 
4.2%
d 408
 
2.9%
Other values (45) 5121
36.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13995
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1514
 
10.8%
a 1032
 
7.4%
r 987
 
7.1%
n 946
 
6.8%
l 915
 
6.5%
o 913
 
6.5%
i 890
 
6.4%
t 678
 
4.8%
s 591
 
4.2%
d 408
 
2.9%
Other values (45) 5121
36.6%

email
Text

MISSING 

Distinct2001
Distinct (%)> 99.9%
Missing2007
Missing (%)50.1%
Memory size31.4 KiB
2024-07-12T08:57:57.099905image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length36
Median length31
Mean length21.938062
Min length13

Characters and Unicode

Total characters43920
Distinct characters39
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2000 ?
Unique (%)99.9%

Sample

1st rowpjosefer0@typepad.com
2nd rowpjosefer0@typepad.com
3rd rowlolorenshaw0@yolasite.com
4th rowlaverall1@umn.edu
5th roweragg2@bizjournals.com
ValueCountFrequency (%)
pjosefer0@typepad.com 2
 
0.1%
tjedraszczykd@ebay.co.uk 1
 
< 0.1%
lolorenshaw0@yolasite.com 1
 
< 0.1%
laverall1@umn.edu 1
 
< 0.1%
eragg2@bizjournals.com 1
 
< 0.1%
fmenloe3@wiley.com 1
 
< 0.1%
rknappen4@admin.ch 1
 
< 0.1%
ecluff5@abc.net.au 1
 
< 0.1%
rblaszkiewicz6@blogspot.com 1
 
< 0.1%
rpeat7@cisco.com 1
 
< 0.1%
Other values (1991) 1991
99.5%
2024-07-12T08:57:57.840327image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 3782
 
8.6%
e 3413
 
7.8%
c 2784
 
6.3%
a 2623
 
6.0%
m 2350
 
5.4%
n 2202
 
5.0%
. 2192
 
5.0%
r 2190
 
5.0%
i 2167
 
4.9%
@ 2002
 
4.6%
Other values (29) 18215
41.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 43920
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 3782
 
8.6%
e 3413
 
7.8%
c 2784
 
6.3%
a 2623
 
6.0%
m 2350
 
5.4%
n 2202
 
5.0%
. 2192
 
5.0%
r 2190
 
5.0%
i 2167
 
4.9%
@ 2002
 
4.6%
Other values (29) 18215
41.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 43920
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 3782
 
8.6%
e 3413
 
7.8%
c 2784
 
6.3%
a 2623
 
6.0%
m 2350
 
5.4%
n 2202
 
5.0%
. 2192
 
5.0%
r 2190
 
5.0%
i 2167
 
4.9%
@ 2002
 
4.6%
Other values (29) 18215
41.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 43920
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 3782
 
8.6%
e 3413
 
7.8%
c 2784
 
6.3%
a 2623
 
6.0%
m 2350
 
5.4%
n 2202
 
5.0%
. 2192
 
5.0%
r 2190
 
5.0%
i 2167
 
4.9%
@ 2002
 
4.6%
Other values (29) 18215
41.5%

first_name
Text

MISSING 

Distinct1762
Distinct (%)88.0%
Missing2007
Missing (%)50.1%
Memory size31.4 KiB
2024-07-12T08:57:58.318814image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length14
Median length12
Mean length5.9895105
Min length2

Characters and Unicode

Total characters11991
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1551 ?
Unique (%)77.5%

Sample

1st rowPietrek
2nd rowPietrek
3rd rowLeslie
4th rowLissy
5th rowEmmaline
ValueCountFrequency (%)
timmy 5
 
0.2%
manolo 4
 
0.2%
kyle 4
 
0.2%
siobhan 3
 
0.1%
dalli 3
 
0.1%
nickie 3
 
0.1%
cornie 3
 
0.1%
roda 3
 
0.1%
mal 3
 
0.1%
hector 3
 
0.1%
Other values (1752) 1969
98.3%
2024-07-12T08:57:59.073557image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e 1481
 
12.4%
a 1267
 
10.6%
i 1051
 
8.8%
n 973
 
8.1%
r 870
 
7.3%
l 844
 
7.0%
o 589
 
4.9%
t 448
 
3.7%
s 326
 
2.7%
y 322
 
2.7%
Other values (44) 3820
31.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11991
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1481
 
12.4%
a 1267
 
10.6%
i 1051
 
8.8%
n 973
 
8.1%
r 870
 
7.3%
l 844
 
7.0%
o 589
 
4.9%
t 448
 
3.7%
s 326
 
2.7%
y 322
 
2.7%
Other values (44) 3820
31.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11991
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1481
 
12.4%
a 1267
 
10.6%
i 1051
 
8.8%
n 973
 
8.1%
r 870
 
7.3%
l 844
 
7.0%
o 589
 
4.9%
t 448
 
3.7%
s 326
 
2.7%
y 322
 
2.7%
Other values (44) 3820
31.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11991
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1481
 
12.4%
a 1267
 
10.6%
i 1051
 
8.8%
n 973
 
8.1%
r 870
 
7.3%
l 844
 
7.0%
o 589
 
4.9%
t 448
 
3.7%
s 326
 
2.7%
y 322
 
2.7%
Other values (44) 3820
31.9%

departement
Categorical

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)0.4%
Missing2007
Missing (%)50.1%
Memory size31.4 KiB
teknik-kendaraan-ringan
250 
teknik-pemesinan
248 
rekayasa-perangkat-lunak
234 
bisnis-kontruksi-dan-properti
223 
teknik-otomasi-industri
222 
Other values (4)
825 

Length

Max length38
Median length29
Mean length27.679321
Min length16

Characters and Unicode

Total characters55414
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowbisnis-kontruksi-dan-properti
2nd rowbisnis-kontruksi-dan-properti
3rd rowteknik-otomasi-industri
4th rowteknik-kendaraan-ringan
5th rowbisnis-kontruksi-dan-properti

Common Values

ValueCountFrequency (%)
teknik-kendaraan-ringan 250
 
6.2%
teknik-pemesinan 248
 
6.2%
rekayasa-perangkat-lunak 234
 
5.8%
bisnis-kontruksi-dan-properti 223
 
5.6%
teknik-otomasi-industri 222
 
5.5%
desain-pemodelan-informasi-bangunan 217
 
5.4%
teknik-fabrikasi-logam-dan-manufaktur 212
 
5.3%
sistem-informasi-jaringan-dan-aplikasi 198
 
4.9%
teknik-komputer-dan-jaringan 198
 
4.9%
(Missing) 2007
50.1%

Length

2024-07-12T08:57:59.385036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:57:59.719238image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
teknik-kendaraan-ringan 250
12.5%
teknik-pemesinan 248
12.4%
rekayasa-perangkat-lunak 234
11.7%
bisnis-kontruksi-dan-properti 223
11.1%
teknik-otomasi-industri 222
11.1%
desain-pemodelan-informasi-bangunan 217
10.8%
teknik-fabrikasi-logam-dan-manufaktur 212
10.6%
sistem-informasi-jaringan-dan-aplikasi 198
9.9%
teknik-komputer-dan-jaringan 198
9.9%

Most occurring characters

ValueCountFrequency (%)
a 7236
13.1%
n 7097
12.8%
i 5647
10.2%
- 5214
9.4%
k 4478
8.1%
e 3614
 
6.5%
r 3292
 
5.9%
s 3033
 
5.5%
t 2862
 
5.2%
o 1932
 
3.5%
Other values (10) 11009
19.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 55414
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 7236
13.1%
n 7097
12.8%
i 5647
10.2%
- 5214
9.4%
k 4478
8.1%
e 3614
 
6.5%
r 3292
 
5.9%
s 3033
 
5.5%
t 2862
 
5.2%
o 1932
 
3.5%
Other values (10) 11009
19.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 55414
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 7236
13.1%
n 7097
12.8%
i 5647
10.2%
- 5214
9.4%
k 4478
8.1%
e 3614
 
6.5%
r 3292
 
5.9%
s 3033
 
5.5%
t 2862
 
5.2%
o 1932
 
3.5%
Other values (10) 11009
19.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 55414
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 7236
13.1%
n 7097
12.8%
i 5647
10.2%
- 5214
9.4%
k 4478
8.1%
e 3614
 
6.5%
r 3292
 
5.9%
s 3033
 
5.5%
t 2862
 
5.2%
o 1932
 
3.5%
Other values (10) 11009
19.9%

education-level
Categorical

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)0.6%
Missing3009
Missing (%)75.1%
Memory size31.4 KiB
diploma3
184 
diploma1/2
180 
diploma4
165 
other
163 
master
156 

Length

Max length10
Median length8
Mean length7.559
Min length5

Characters and Unicode

Total characters7559
Distinct characters19
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdiploma1/2
2nd rowother
3rd rowdiploma4
4th rowdiploma1/2
5th rowdiploma1/2

Common Values

ValueCountFrequency (%)
diploma3 184
 
4.6%
diploma1/2 180
 
4.5%
diploma4 165
 
4.1%
other 163
 
4.1%
master 156
 
3.9%
bachelor 152
 
3.8%
(Missing) 3009
75.1%

Length

2024-07-12T08:58:00.062108image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:58:00.351036image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
diploma3 184
18.4%
diploma1/2 180
18.0%
diploma4 165
16.5%
other 163
16.3%
master 156
15.6%
bachelor 152
15.2%

Most occurring characters

ValueCountFrequency (%)
o 844
11.2%
a 837
11.1%
m 685
9.1%
l 681
9.0%
d 529
 
7.0%
p 529
 
7.0%
i 529
 
7.0%
r 471
 
6.2%
e 471
 
6.2%
t 319
 
4.2%
Other values (9) 1664
22.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7559
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 844
11.2%
a 837
11.1%
m 685
9.1%
l 681
9.0%
d 529
 
7.0%
p 529
 
7.0%
i 529
 
7.0%
r 471
 
6.2%
e 471
 
6.2%
t 319
 
4.2%
Other values (9) 1664
22.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7559
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 844
11.2%
a 837
11.1%
m 685
9.1%
l 681
9.0%
d 529
 
7.0%
p 529
 
7.0%
i 529
 
7.0%
r 471
 
6.2%
e 471
 
6.2%
t 319
 
4.2%
Other values (9) 1664
22.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7559
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 844
11.2%
a 837
11.1%
m 685
9.1%
l 681
9.0%
d 529
 
7.0%
p 529
 
7.0%
i 529
 
7.0%
r 471
 
6.2%
e 471
 
6.2%
t 319
 
4.2%
Other values (9) 1664
22.0%

institution
Text

MISSING 

Distinct947
Distinct (%)94.7%
Missing3009
Missing (%)75.1%
Memory size31.4 KiB
2024-07-12T08:58:00.929777image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Length

Max length94
Median length65
Mean length31.188
Min length11

Characters and Unicode

Total characters31188
Distinct characters87
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique896 ?
Unique (%)89.6%

Sample

1st rowOglala Lakota College
2nd rowUniversity of Engineering and Technology Lahore
3rd rowPukyong National University
4th rowInternational Colleges of Islamic Science
5th rowHokkaido Institute of Technology
ValueCountFrequency (%)
university 538
 
13.5%
of 285
 
7.2%
college 134
 
3.4%
de 120
 
3.0%
universidad 82
 
2.1%
state 76
 
1.9%
institute 59
 
1.5%
technology 55
 
1.4%
and 47
 
1.2%
national 36
 
0.9%
Other values (1430) 2552
64.1%
2024-07-12T08:58:01.849291image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3003
 
9.6%
i 2888
 
9.3%
e 2804
 
9.0%
n 2168
 
7.0%
a 2095
 
6.7%
t 1815
 
5.8%
r 1676
 
5.4%
o 1622
 
5.2%
s 1575
 
5.1%
l 1170
 
3.8%
Other values (77) 10372
33.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 31188
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3003
 
9.6%
i 2888
 
9.3%
e 2804
 
9.0%
n 2168
 
7.0%
a 2095
 
6.7%
t 1815
 
5.8%
r 1676
 
5.4%
o 1622
 
5.2%
s 1575
 
5.1%
l 1170
 
3.8%
Other values (77) 10372
33.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 31188
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3003
 
9.6%
i 2888
 
9.3%
e 2804
 
9.0%
n 2168
 
7.0%
a 2095
 
6.7%
t 1815
 
5.8%
r 1676
 
5.4%
o 1622
 
5.2%
s 1575
 
5.1%
l 1170
 
3.8%
Other values (77) 10372
33.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 31188
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3003
 
9.6%
i 2888
 
9.3%
e 2804
 
9.0%
n 2168
 
7.0%
a 2095
 
6.7%
t 1815
 
5.8%
r 1676
 
5.4%
o 1622
 
5.2%
s 1575
 
5.1%
l 1170
 
3.8%
Other values (77) 10372
33.3%

Program-of-Study
Categorical

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)1.2%
Missing3009
Missing (%)75.1%
Memory size31.4 KiB
Human Resources
94 
Legal
93 
Product Management
92 
Marketing
90 
Services
85 
Other values (7)
546 

Length

Max length24
Median length15
Mean length11.45
Min length5

Characters and Unicode

Total characters11450
Distinct characters30
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLegal
2nd rowEngineering
3rd rowProduct Management
4th rowProduct Management
5th rowServices

Common Values

ValueCountFrequency (%)
Human Resources 94
 
2.3%
Legal 93
 
2.3%
Product Management 92
 
2.3%
Marketing 90
 
2.2%
Services 85
 
2.1%
Training 85
 
2.1%
Support 83
 
2.1%
Engineering 82
 
2.0%
Accounting 80
 
2.0%
Business Development 78
 
1.9%
Other values (2) 138
 
3.4%
(Missing) 3009
75.1%

Length

2024-07-12T08:58:02.193457image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
development 142
 
10.2%
human 94
 
6.8%
resources 94
 
6.8%
legal 93
 
6.7%
product 92
 
6.6%
management 92
 
6.6%
marketing 90
 
6.5%
services 85
 
6.1%
training 85
 
6.1%
support 83
 
6.0%
Other values (6) 442
31.8%

Most occurring characters

ValueCountFrequency (%)
e 1595
13.9%
n 1228
 
10.7%
a 748
 
6.5%
r 675
 
5.9%
i 667
 
5.8%
s 645
 
5.6%
g 604
 
5.3%
t 579
 
5.1%
u 521
 
4.6%
c 495
 
4.3%
Other values (20) 3693
32.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11450
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1595
13.9%
n 1228
 
10.7%
a 748
 
6.5%
r 675
 
5.9%
i 667
 
5.8%
s 645
 
5.6%
g 604
 
5.3%
t 579
 
5.1%
u 521
 
4.6%
c 495
 
4.3%
Other values (20) 3693
32.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11450
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1595
13.9%
n 1228
 
10.7%
a 748
 
6.5%
r 675
 
5.9%
i 667
 
5.8%
s 645
 
5.6%
g 604
 
5.3%
t 579
 
5.1%
u 521
 
4.6%
c 495
 
4.3%
Other values (20) 3693
32.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11450
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1595
13.9%
n 1228
 
10.7%
a 748
 
6.5%
r 675
 
5.9%
i 667
 
5.8%
s 645
 
5.6%
g 604
 
5.3%
t 579
 
5.1%
u 521
 
4.6%
c 495
 
4.3%
Other values (20) 3693
32.3%

product-or-service-offered
Categorical

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)2.2%
Missing3009
Missing (%)75.1%
Memory size31.4 KiB
Movies
 
60
Baby
 
57
Electronics
 
56
Jewelry
 
52
Sports
 
52
Other values (17)
723 

Length

Max length11
Median length9
Mean length6.436
Min length4

Characters and Unicode

Total characters6436
Distinct characters34
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowOutdoors
2nd rowSports
3rd rowMovies
4th rowToys
5th rowMovies

Common Values

ValueCountFrequency (%)
Movies 60
 
1.5%
Baby 57
 
1.4%
Electronics 56
 
1.4%
Jewelry 52
 
1.3%
Sports 52
 
1.3%
Books 51
 
1.3%
Games 49
 
1.2%
Automotive 48
 
1.2%
Clothing 48
 
1.2%
Garden 47
 
1.2%
Other values (12) 480
 
12.0%
(Missing) 3009
75.1%

Length

2024-07-12T08:58:02.476496image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
movies 60
 
6.0%
baby 57
 
5.7%
electronics 56
 
5.6%
jewelry 52
 
5.2%
sports 52
 
5.2%
books 51
 
5.1%
games 49
 
4.9%
automotive 48
 
4.8%
clothing 48
 
4.8%
garden 47
 
4.7%
Other values (12) 480
48.0%

Most occurring characters

ValueCountFrequency (%)
o 756
 
11.7%
e 605
 
9.4%
s 583
 
9.1%
t 443
 
6.9%
r 411
 
6.4%
i 340
 
5.3%
l 282
 
4.4%
a 277
 
4.3%
u 235
 
3.7%
y 232
 
3.6%
Other values (24) 2272
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 756
 
11.7%
e 605
 
9.4%
s 583
 
9.1%
t 443
 
6.9%
r 411
 
6.4%
i 340
 
5.3%
l 282
 
4.4%
a 277
 
4.3%
u 235
 
3.7%
y 232
 
3.6%
Other values (24) 2272
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 756
 
11.7%
e 605
 
9.4%
s 583
 
9.1%
t 443
 
6.9%
r 411
 
6.4%
i 340
 
5.3%
l 282
 
4.4%
a 277
 
4.3%
u 235
 
3.7%
y 232
 
3.6%
Other values (24) 2272
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6436
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 756
 
11.7%
e 605
 
9.4%
s 583
 
9.1%
t 443
 
6.9%
r 411
 
6.4%
i 340
 
5.3%
l 282
 
4.4%
a 277
 
4.3%
u 235
 
3.7%
y 232
 
3.6%
Other values (24) 2272
35.3%

amount-of-profit
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing3009
Missing (%)75.1%
Memory size31.4 KiB

unit-of-profit
Categorical

HIGH CORRELATION  MISSING 

Distinct3
Distinct (%)0.3%
Missing3009
Missing (%)75.1%
Memory size31.4 KiB
week
350 
month
331 
day
319 

Length

Max length5
Median length4
Mean length4.012
Min length3

Characters and Unicode

Total characters4012
Distinct characters11
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowweek
2nd rowweek
3rd rowmonth
4th rowday
5th rowmonth

Common Values

ValueCountFrequency (%)
week 350
 
8.7%
month 331
 
8.3%
day 319
 
8.0%
(Missing) 3009
75.1%

Length

2024-07-12T08:58:02.777724image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-07-12T08:58:03.071858image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
ValueCountFrequency (%)
week 350
35.0%
month 331
33.1%
day 319
31.9%

Most occurring characters

ValueCountFrequency (%)
e 700
17.4%
w 350
8.7%
k 350
8.7%
m 331
8.3%
o 331
8.3%
n 331
8.3%
t 331
8.3%
h 331
8.3%
d 319
8.0%
a 319
8.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4012
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 700
17.4%
w 350
8.7%
k 350
8.7%
m 331
8.3%
o 331
8.3%
n 331
8.3%
t 331
8.3%
h 331
8.3%
d 319
8.0%
a 319
8.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4012
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 700
17.4%
w 350
8.7%
k 350
8.7%
m 331
8.3%
o 331
8.3%
n 331
8.3%
t 331
8.3%
h 331
8.3%
d 319
8.0%
a 319
8.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4012
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 700
17.4%
w 350
8.7%
k 350
8.7%
m 331
8.3%
o 331
8.3%
n 331
8.3%
t 331
8.3%
h 331
8.3%
d 319
8.0%
a 319
8.0%

Interactions

2024-07-12T08:57:31.206103image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:27.948907image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:29.037348image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:30.142160image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:31.481561image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:28.238401image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:29.305360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:30.406489image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:31.756653image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:28.511360image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:29.579965image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:30.671995image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:32.049530image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:28.774733image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:29.880086image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
2024-07-12T08:57:30.948596image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/

Correlations

2024-07-12T08:58:03.630099image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Program-of-Studyamount-of-salary-and-allowncedepartementdepartmenteducation-levelemployment-statusgendergraduation-yearimprovement-suggestionsindustry-sectormonthnikpositionproduct-or-service-offeredquality-of-educationstatusunit-of-profityear
Program-of-Study1.000NaN0.0000.0000.0000.0000.0000.0400.0000.0000.0000.0160.0000.0000.0001.0000.000NaN
amount-of-salary-and-allownceNaN1.0001.0000.0910.0000.0000.924-0.0010.9990.9970.1710.0010.9970.0000.0681.0000.000-0.023
departement0.0001.0001.0000.0000.0001.0000.0000.0250.0000.0000.033-0.0310.0000.0000.0001.0000.0000.010
department0.0000.0910.0001.0000.0420.0910.3530.0140.5750.0910.057-0.0120.0910.0410.0540.7050.000-0.006
education-level0.0000.0000.0000.0421.0000.0000.0120.0360.0000.0000.000-0.0160.0000.0000.0001.0000.000NaN
employment-status0.0000.0001.0000.0910.0001.0000.3720.0390.9990.9970.0000.0380.9970.0001.0001.0000.0000.039
gender0.0000.9240.0000.3530.0120.3721.000-0.0010.3530.3510.029-0.0020.3520.0660.0000.0350.0000.015
graduation-year0.040-0.0010.0250.0140.0360.039-0.0011.0000.1240.0000.0280.0230.0311.0000.0730.1231.000-0.008
improvement-suggestions0.0000.9990.0000.5750.0000.9990.3530.1241.0000.6290.334-0.0040.6300.0000.8020.7060.0000.012
industry-sector0.0000.9970.0000.0910.0000.9970.3510.0000.6291.0000.0390.0010.3920.0000.5771.0000.0000.001
month0.0000.1710.0330.0570.0000.0000.0290.0280.3340.0391.000-0.0080.0570.0250.3900.0000.0390.022
nik0.0160.001-0.031-0.012-0.0160.038-0.0020.023-0.0040.001-0.0081.0000.9971.0000.0410.0191.0000.006
position0.0000.9970.0000.0910.0000.9970.3520.0310.6300.3920.0570.9971.0000.0000.5791.0000.0000.038
product-or-service-offered0.0000.0000.0000.0410.0000.0000.0661.0000.0000.0000.0251.0000.0001.0000.0001.0000.000-0.021
quality-of-education0.0000.0680.0000.0540.0001.0000.0000.0730.8020.5770.3900.0410.5790.0001.0000.4630.000-0.023
status1.0001.0001.0000.7051.0001.0000.0350.1230.7061.0000.0000.0191.0001.0000.4631.0001.000-0.012
unit-of-profit0.0000.0000.0000.0000.0000.0000.0001.0000.0000.0000.0391.0000.0000.0000.0001.0001.0000.005
yearNaN-0.0230.010-0.006NaN0.0390.015-0.0080.0120.0010.0220.0060.038-0.021-0.023-0.0120.0051.000

Missing values

2024-07-12T08:57:32.541931image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
A simple visualization of nullity by column.
2024-07-12T08:57:33.484277image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-07-12T08:57:34.548203image/svg+xmlMatplotlib v3.7.1, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

_idemployment-statusfirst-jobyearbirthdategraduation-yearcompany-nameindustry-sectorAddressquality-of-educationlast-namedepartmentstatuspositionopinion-of-the-field-of-studygendermonthsalary-meet-the-minimum-wageimprovement-suggestionsfirst-namenikamount-of-salary-and-allownceother-positionlast_nameemailfirst_namedepartementeducation-levelinstitutionProgram-of-Studyproduct-or-service-offeredamount-of-profitunit-of-profit
0{'$oid': '66903c606e398b6e105dcff8'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesaegbahwg9yhagwMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
1{'$oid': '66903d9c6e398b6e105dcff9'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesaegbahwg9yhagwMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
2{'$oid': '6690406e6e398b6e105dcffa'}permanentyes2024.02006-06-042024PT IjoroyootherCibinongexcellentIsmailrekayasa-perangkat-lunakworkingotherusefulmaleaprilyesoiufyutycuvbjoigouiyctuFadli10230731970980975000000.0pkojihgufyudtyrstuiyguoipjNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
3{'$oid': '66909195b72fc3ddba24dd30'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesimprove the cleaning servicesMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
4{'$oid': '66909195b72fc3ddba24dd31'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesaegbahwg9yhagwMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
5{'$oid': '669099f7b72fc3ddba24dd32'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesimprove the cleaning servicesMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
6{'$oid': '669099f7b72fc3ddba24dd33'}daily-freelanceyes2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberyesaegbahwg9yhagwMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaNNaN
7{'$oid': '66909acfb72fc3ddba24dd34'}daily-freelanceFalse1992.02024-03-181998Cardinal HealthFinanceSuite 100poorNaNNaNworkingBusiness DevelopmentNaNNon-binaryjulyFalsehard to get a job for a whileNaN36426311399351.0NaNJoseferpjosefer0@typepad.comPietrekbisnis-kontruksi-dan-propertiNaNNaNNaNNaNNaNNaN
8{'$oid': '66909addb72fc3ddba24dd35'}daily-freelanceFalse1992.02024-03-181998Cardinal HealthFinanceSuite 100poorNaNNaNworkingBusiness DevelopmentNaNNon-binaryjulyFalsehard to get a job for a whileNaN36426311399351.0NaNJoseferpjosefer0@typepad.comPietrekbisnis-kontruksi-dan-propertiNaNNaNNaNNaNNaNNaN
9{'$oid': '66909eb3b72fc3ddba24dd36'}daily-freelanceFalse1994.02023-07-092015NCS HealthCare of KY, Inc dba Vangard Labsn/a16th FloorgoodNaNNaNworkingAccountingNaNMaleaprilTrueit's hard for getting job after school lifeNaN67478990321101.0NaNOlorenshawlolorenshaw0@yolasite.comLeslieteknik-otomasi-industriNaNNaNNaNNaNNaNNaN
_idemployment-statusfirst-jobyearbirthdategraduation-yearcompany-nameindustry-sectorAddressquality-of-educationlast-namedepartmentstatuspositionopinion-of-the-field-of-studygendermonthsalary-meet-the-minimum-wageimprovement-suggestionsfirst-namenikamount-of-salary-and-allownceother-positionlast_nameemailfirst_namedepartementeducation-levelinstitutionProgram-of-Studyproduct-or-service-offeredamount-of-profitunit-of-profit
3999{'$oid': '6690b2c1b72fc3ddba24eccc'}NaNNaN2003.02024-05-252004NaNNaNNaNnot-satisfiedLoosemoreMarketingentrepreneurNaNNaNMalejulyNaNsaya sangat sulit untuk masuk kuliahBasilius3778678078NaNNaNNaNNaNNaNNaNNaNNaNNaNBooks{'$numberLong': '55788804323'}day
4000{'$oid': '6690b2c1b72fc3ddba24eccd'}NaNNaN2004.02023-10-261995NaNNaNNaNpoorOusleyServicesentrepreneurNaNNaNFemaleaugustNaNsaya bisa masuk kuliah walaupun harus bersusah payahCorine616913931NaNNaNNaNNaNNaNNaNNaNNaNNaNJewelry{'$numberLong': '21867860224'}week
4001{'$oid': '6690b2c1b72fc3ddba24ecce'}NaNNaN1995.02024-05-131994NaNNaNNaNexcellentSleneyProduct ManagemententrepreneurNaNNaNAgenderseptemberNaNsaya berhasil masuk ptn impianGabrila5821594561NaNNaNNaNNaNNaNNaNNaNNaNNaNBaby{'$numberLong': '2510993517'}day
4002{'$oid': '6690b2c1b72fc3ddba24eccf'}NaNNaN2009.02023-11-182006NaNNaNNaNgoodSpurmanHuman ResourcesentrepreneurNaNNaNFemaleoctoberNaNsaya berhasil masuk ptn walaupun bukan impian sayaAurilia8847250064NaNNaNNaNNaNNaNNaNNaNNaNNaNShoes{'$numberLong': '25427321398'}week
4003{'$oid': '6690b2c1b72fc3ddba24ecd0'}NaNNaN1987.02024-03-142008NaNNaNNaNnot-satisfiedWickieEngineeringentrepreneurNaNNaNMalenovemberNaNsaya sangat sulit untuk masuk kuliahDunc7627887798NaNNaNNaNNaNNaNNaNNaNNaNNaNBaby{'$numberLong': '26741140991'}month
4004{'$oid': '6690b2c1b72fc3ddba24ecd1'}NaNNaN1979.02024-02-261999NaNNaNNaNpoorLuckwellSalesentrepreneurNaNNaNMaledecemberNaNsaya bisa masuk kuliah walaupun harus bersusah payahJessee6264888060NaNNaNNaNNaNNaNNaNNaNNaNNaNTools{'$numberLong': '28146607148'}month
4005{'$oid': '6690b2c1b72fc3ddba24ecd2'}NaNNaN2001.02023-08-072005NaNNaNNaNexcellentFirbyBusiness DevelopmententrepreneurNaNNaNFemalejanuaryNaNsaya berhasil masuk ptn impianBonnibelle3522841859NaNNaNNaNNaNNaNNaNNaNNaNNaNGrocery{'$numberLong': '63713198961'}month
4006{'$oid': '6690b2c1b72fc3ddba24ecd3'}NaNNaN2009.02023-07-312008NaNNaNNaNgoodSwaitSalesentrepreneurNaNNaNMalefebruaryNaNsaya berhasil masuk ptn walaupun bukan impian sayaLucho9793330066NaNNaNNaNNaNNaNNaNNaNNaNNaNToys{'$numberLong': '76658580502'}day
4007{'$oid': '6690b2c1b72fc3ddba24ecd4'}NaNNaN2003.02024-02-271993NaNNaNNaNnot-satisfiedDouganProduct ManagemententrepreneurNaNNaNFemalemarchNaNsaya sangat sulit untuk masuk kuliahCiel8744184816NaNNaNNaNNaNNaNNaNNaNNaNNaNShoes{'$numberLong': '44754979229'}month
4008{'$oid': '6690b2c1b72fc3ddba24ecd5'}NaNNaN2011.02023-12-242010NaNNaNNaNpoorNesbittSupportentrepreneurNaNNaNFemaleaprilNaNsaya bisa masuk kuliah walaupun harus bersusah payahOliy2715726457NaNNaNNaNNaNNaNNaNNaNNaNNaNTools{'$numberLong': '66791719424'}week

Duplicate rows

Most frequently occurring

employment-statusyearbirthdategraduation-yearcompany-nameindustry-sectorAddressquality-of-educationlast-namedepartmentstatuspositionopinion-of-the-field-of-studygendermonthimprovement-suggestionsfirst-namenikamount-of-salary-and-allownceother-positionlast_nameemailfirst_namedepartementeducation-levelinstitutionProgram-of-Studyproduct-or-service-offeredunit-of-profit# duplicates
1daily-freelance2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberaegbahwg9yhagwMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN4
0daily-freelance1992.02024-03-181998Cardinal HealthFinanceSuite 100poorNaNNaNworkingBusiness DevelopmentNaNNon-binaryjulyhard to get a job for a whileNaN36426311399351.0NaNJoseferpjosefer0@typepad.comPietrekbisnis-kontruksi-dan-propertiNaNNaNNaNNaNNaN2
2daily-freelance2024.02001-01-152001JIVALOKAmarketingJl . Bumi Karadenan PermaigoodSeptanugrohoteknik-otomasi-industriworkingaccountingusefulmaleoctoberimprove the cleaning servicesMuhammad Aris3201010439710924120834021.0NaNNaNNaNNaNNaNNaNNaNNaNNaNNaN2